Maya-Nidra P2: Adaptive Vairagya and Dual-Phase REM/NREM Consolidation in Neuromorphic EEG Sleep Staging — Full Clinical Validation on Sleep-EDF Expanded

Maya-Nidra P2 extends the neuromorphic sleep staging system introduced in P1 by scaling training to the full Sleep-EDF Expanded dataset (415,961 epochs across 153 recordings), introducing Adaptive Vairagya decay — a biologically motivated per-stage learning rate modulation — and separating memory replay into dedicated REM and NREM ring buffers to capture dual-phase consolidation dynamics. The central architectural finding is that Vairagya decay applied during live training batches destabilises spiking neural network convergence, whereas restricting decay to the replay phase — mirroring the biological role of sleep rather than waking plasticity — recovers stable learning, establishing a principled placement for decay in neuromorphic training pipelines. On 10,000 balanced training epochs, MayaEEGNet (583,847 parameters, architecture unchanged from P1) achieves 52.63% overall accuracy with WAKE F1 reaching 82.7%, a +20.2 percentage-point improvement over P1, and the dual-phase replay produces a REM/NREM consolidation ratio of 4.3200 compared to a P1 baseline of 0.0000 — confirming that buffer separation introduces measurable asymmetric consolidation dynamics. This record marks the second confirmation of both Maya-Nidra series constants in the neurophysiological domain, with results reported as part of the broader Maya-Nidra Series on neuromorphic AI for clinical sleep medicine. The work is conducted at Nexus Learning Labs, Bengaluru, India, contributing neuromorphic sleep intelligence research from an Indian institution to the global clinical AI community. Series: Part of the Maya-Nidra Series — neuromorphic AI for clinical sleep medicine. Both series constants confirmed in neurophysiological domain. Links: GitHub Repository (private — to request access: email research@nexuslearninglabs.in with subject Code Access Request — Maya-Nidra-P2 and your research context) | Interactive Dashboard | FAQ | Full Series Index — venky2099.github.io Nexus Learning Labs, Bengaluru · UDYAM-KR-02-0122422 · BHASKAR IN-0526-9452JSORCID: 0000-0002-3315-7907 · VAIRAGYA_DECAY_RATE = 0.002315 (embedded in all canonical hyperparameters)Canary: MayaNexusVS2026NLL_Bengaluru_Narasimha